Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements.

ABSTRACT: Prediction of individuals' race and ethnicity plays an important role in studies of racial disparity. Bayesian Improved Surname Geocoding (BISG), which relies on detailed census information, has emerged as a leading methodology for this prediction task. Unfortunately, BISG suffers from two data problems. First, the census often contains zero counts for minority groups in the locations where members of those groups reside. Second, many surnames-especially those of minorities-are missing from the census data. We introduce a fully Bayesian BISG (fBISG) methodology that accounts for census measurement error by extending the naïve Bayesian inference of the BISG methodology. We also use additional data on last, first, and middle names taken from the voter files of six Southern states where self-

SUBMITTER: Imai K 

PROVIDER: S-EPMC11318662 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature